Reversible Digital Image Watermarking Scheme Using Bit Replacement and Majority Algorithm Technique

doi:10.4236/jilsa.2012.43020

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Journal of Intelligent Learning Systems and Applications, 2012, 4, 199-206

doi:10.4236/jilsa.2012.43020 Published Online August 2012 (http://www.SciRP.org/journal/jilsa) 1

Reversible Digital Image Watermarking Scheme Using Bit

Replacement and Majority Algorithm Technique

Koushik Pal1, Goutam Ghosh1, Mahua Bhattacharya2*

1Institute of Radio Physics and Electronics, University of Calcutta, Kolkata, India; 2Indian Institute of Information Technology and

Management, Gwalior, India.

Email: *[email protected]

Received September 24th, 2011; revised May 17th, 2012; accepted May 25th, 2012

ABSTRACT

The current paper presents a new digital watermarking method through bit replacement technology, which stores multi-

ple copies of the same data that is to be hidden in a scrambled form in the cover image. In this paper an indigenous ap-

proach is described for recovering the data from the damaged copies of the data under attack by applying a majority

algorithm to find the closest twin of the embedded information. A new type of non-oblivious detection method is also

proposed. The improvement in performance is supported through experimental results which show much enhancement

in the visual and statistical invisibility o f hidden data.

Keywords: Additive Noise; Salt and Pepper Noise; Compression; Filtering; Averaging; Multiple Watermarking;

Majority Algorithm

1. Introduction

Recent history has witnessed the rapid development in

information technologies that has given an extended and

easy access to digital information. Along with several

developments it leads to the problem of illegal copying

and redistribution of digital media. As a result, the integ-

rit y and confidentiality of the digital information has come

under immense threat. Digital watermarking, an e mergi ng

technology, came in order to solve the problems [1].

Digital watermarking is a technique which allows an

individual to add hidden copyright notices or other veri-

fication messages or even classified information into

digital media [2,3].

Watermarks can either be visible or invisible. Here in

this paper we utilize the invisible watermarking tech-

nique. This is used in public in formation settings such as

digital image libraries, museums, and art galleries and

also in defense communication where data security is of

prime importance [4]. Watermark embedding utilizes

two kinds of methods, one in the spatial domain and the

other in th e transfor m domain. In th e spatial doma in the

watermark is directly embedded into the image pixels

whereas in the frequency domain the image is decom-

posed into blocks and then mapped into the transform

domain [5].

Watermarking is basically a process of hiding infor-

mation in an image known as cover image. According to

the behavior it is of two types: robust and fragile. Copy-

right protection is achieved by robust watermarking [6]

while image authentication is usually achieved b y fragile

watermarking techniques [7-9]. Robust watermarking

technique ensures the quality of the hidden information

by several protection algorithms. In the fragile water-

marking scheme if any alter ation of the message is found

then the message breaks up by itself and can be easily

detected as tampered by the provider of the watermark

[1]. Invertible watermarking is a new process which en-

ables the exact recovery of the original image upon ex-

traction of the embedded info rmation.

In the present paper the work implements both authen-

tication and confidentiality in a reversible [10] manner

without affecting the image in any way. Security of im-

ages imposes three mandatory characteristics: confiden-

tiality, reliability and availability [11]. Confidentiality

means that only the entitled persons have access to the

images. Reliability has two aspects: 1) Integrity—means

the image has not been modified by a non-authorized

person [12]; 2) Authentication corroborates that the im-

age belongs indeed to the correct person and is issued

from an authorized source. Availability is the capacity

that an image is available to the entitled persons in the

normal conditions of access and exercise [13].

1.1. LSB Watermarking and Its Limitation

The most straight-forward method of watermark embed-

ding would be to embed the watermark into the least-

*Corresponding a uthor.

Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique

200

significant-bits (LSB) of the cover object [14]. Given the

extraordinarily high channel capacity of using the entire

cover for transmission in this method, a smaller object

may be embedded multiple times [8]. Even if most of

these are lost due to attacks, a single surviving water-

mark would be considered as a success. LSB substitution,

however, despite its simplicity brings a lot of drawbacks.

Although it may survive from all these transformations

such as cropping, addition of noise or lossy compression

etc. A better attack would be to simply set the LSB bits

of each pixel to defeat the watermark with negligible

impact on the cover object. Furthermore, once the algo-

rithm is discovered, the embedded watermark could be

easily modified by an intermediate party. An improve-

ment on basic LSB substitution would be to use a pseu-

do-random number generator to determine the pixels to

be used for embedding based on a given “seed” or key

[1]. The algorithm however would still be vulnerable to

replacing the LSB’s with a constant. Even locations that

were not used for the watermarking bits, the impact of

the substitution on the cover image would be negligible.

LSB modification proves to be a simple and fairly power-

ful tool for steganography [15], however, it lacks the

basic robustness that watermarking applications require.

1.2. Attack and Distortion

In practice, a watermarked image may be altered either

on purpose or accidentally. The watermarking system

should be robust enough to detect and extract the water-

mark [16]. Different types of alterations or attacks can be

done to degrade the image quality by adding distortions.

The distortions are limited to those factors which do

not produce excessive degradations; otherwise the trans-

formed object would be unusable. These distortions also

introduce degradation on the performance of the water-

mark extraction algorithm [17,18]. Methods or a combi-

nation of methods, considered unintentional are used

intentionally as an attack on a watermarked image in

order to render the watermark undetectable.

Compression is a common attack, as data transferred

via network is often compressed using JPEG. High qual-

ity images are often converted to JPEG to reduce their

size. Another method is deletion or shuffling of blocks.

In images rows or columns of pixels may be deleted or

shuffled without a noticeable degradation in image quality.

These may render an existing watermark undetectable.

Salt and pepper noise is another type of attack that re-

places the intensity levels of some of the pixels of an

image resulting in loss of information from those pixels.

Some of the best known attacks are mentioned here; they

may be intentional or unintentional, depending on the

application.

In this paper we have taken two very popular attacks

known as Salt and pepper noise and image compression.

2. Proposed Watermarking Technique for

Data Authentication

Our proposed methodology for data hiding does not

follow the conventional LSB technique because of the

inherent limitations mentioned earlier. A new digital water-

marking scheme described here, uses several bits of the

cover image starting from the lower order to the higher

order to hide the information logo. Several sets of the

same data forming the information logo are hidden into

the cover image. Thus, even if some of the information is

lost due to attack, we can still collect the remaining

information from the cover image and reconstruct the

hidden information resembling the original one [19].

The detail algorithm for both the embedding scheme

and the recovery scheme is given bellow.

2.1. Embedding the Digital Watermark

Step 1: Two images are taken as input: First of all, the

cover image and the message or information logo are

taken as inputs. The cover image is taken to be a gray

scale image. The logo or information is a binary image,

basically a sequence of 0’s and 1’s.

Step 2: The Size of the images is extracted: Next to

make the program compatible to run for any size of the

cover image and information logo keeping in mind the

data carrying capacity of the cover image the dimensions

of the respective images are extracted.

Mc=size(cover_image,1);Nc=size(cover_image,2);

Mm=size(logo,1);Nm=size(logo,2);

Step 3: Normalizing and reshaping the logo: After

normalizing the information logo it is reshaped in one

dimension.

logo_nor=logo/.256;

Step 4: Transforming the cover image into wavelet

domain using dwt: The cover image is transformed to

the wavelet domain u sing discrete wavelet tran sform. W e

use Haar transform to do the DWT. Here the 1st level

DWT was used to obtain more capacity for hiding the

information. The cover image is decomposed into 4 sub-

domains as HH, HL, LH and LL according to the different

frequencies of t he cover image.

[HHi,HLi,LHi,LLi] = dwt2(cover_image,'haar');

Step 5: Calculating the length of the transformed

cover image and 1d l og o:

len_logo=length(logo_res);

len_cover=Mc*Nc;

Step 6: Calculating the size of each sub domain de-

composed cover image and reshaping them in to 1d:

cover_HL_size_m=size(HLi,1);

cover_HL_size_n=size(HLi,2);

HL1=reshape(HLi,1,cover_HL_size_m*cover_HL_siz

e_n);

Step 7: Det ermining th e maximum co efficient value

Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique 201

of each of the 4 sub dom ains

max_HL1=max(HL1);

Step 8: Finding the position to hide the information

logo into the transformed logo: The position for hiding

the binary logo in each sub domain must be in between

zero and t he maxim um coefficient val ue of t hat sub dom ain.

c=1;

for i = 1: 1 : cover_HL_size_m*cover_HL_size_n

if(HL1(i)<(max_ HL1) && HL1(i)>(0))

pos_ HL1(c)=i;

c=c+1;

end

Step 9: Hiding a number of sets of the same infor-

mation logo in hl and lh domain: More than one set of

the same information is being hidden in HL and LH band

or domain for easier and good quality recovery. The hid-

ing process in each of these domains follows a specific

formula. The formula is that the black dots in each set of

1d information logo is hidden in a position of informa-

tion logo from where a constant value is subtracted.

a=15

for i = 1: 1 : length_of_HL

if logo_res(i)==0

w_HL1(pos_HL1_set_1(i))=HL1(pos_HL1_set_1(i))-a;

end

Step10: Reshaping the decomposed image back to

its normal dimension:

w_cH1_reshape=reshape(w_cH1,cover_cH_size_m,co

ver_cH_size_n);

Step11: Writing the watermarked image to a file

and displaying it.

2.2. Recovery of the Embedded Watermark

We have assumed that the cover image hiding the wa-

termark is available at the receiving end. So again, in the

process of recovery we first take the original image used

to hide the information. Along with it we also send the

receiver of the message, 3 keys which essentially act as

private keys. These keys are required to decrypt and to

the extract the encrypted or embedded messages.

Step 1: The watermarked and original images are

taken as inputs.

Step2: Finding the 1st level decomposition of both

the inputs using DWT:

[HHw,HLw,LHw,LLw]=dwt2(watermarked_image,'ha

ar');

[HHi,HLi,LHi,LLi] = dwt2(orig_image,'haar');

Step 3: Finding the size of each sub domain of both

the decomposed input images:

orig_HL_size_m=size(HLi,1);

orig_HL_size_n=size(HLi,2);

watermarked_HL_size_m=size(HLw,1);

watermarked_HL_size_n=size(HLw,2);

Step 4: Reshaping each of the decomposition of

both watermarked and original cover images into 1D:

HLo=reshape(HLi,1,orig_HL_size_m*orig_HL_size_

n);

HL1=reshape(HLw,1,watermarked_HL_size_m*water

marked_HL_size_n);

Step 5: Taking the two input keys equal to the di-

mension of the logo to find the size o f the 4 decompo-

sitions of the logo:

key_M=input('Enter the no of rows KEY 1 :');

key_N=input('Enter the no of cols KEY 2:');

logo_HL_size_m=key_M/2;

logo_HL_size_n=key_N/2;

Step 6: Determining the maximum coefficient val-

ues of the original cover image:

max_cHo=max(cHo);

Step 7: Finding the positions used to hide the logo

for each decom posit ion:

c=1;

for i = 1: 1 : orig_HL_size_m*orig_cA_size_n

if(HLo(i)<(max_HLo) && HLo(i)>(0))

pos_HLo(c)=i;

c=c+1;

end

Step 8: Extracting the positional sets for different

sets of the logo from each decomposition:

c=1;

for i = 1 : 1 : len_set_HLo

pos_HLo_set_1(i)=pos_HLo(c);

c=c+1;

Step 9: Recovery of the different sets of logo from

each of the sub bands and construction of the final

logo from the different recovered sets using majority

algorithm:

for i = 1: 1 : key_M*key_N

if(rnd_HL1(pos_HLo_set_3(i))-rnd_HLo(pos_HLo_set_3

(i)))==0

rec_wmark_V_set_3(i)=1;

else

rec_wmark_V_set_3(i)=0;

end

Step 10: After reshaping, displaying each of the re-

covered sets of logo and the final constructed logo:

res_rec_wmark_H_set_3=reshape(rec_wmark_H_set_

3,key_M,key_N);

if rec_wmark_V_set_3(i)==1

val_1=val_1+1;

end

rec_wmark_H_FINAL(i)=val;

Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique

202

res_rec_wmark_H_FINAL=reshape(rec_wmark_H_FI

NAL,key_M,key_N);

2.3. Design Flow of the Proposed Scheme

Embedding Technique:

Gray scale co v er image (CI) and binary

logo (information ) taken as input

Extracting the image size and

reshaping both images to 1D form

Determine min intensity of CI (threshold)

and zero intensity positions o f logo

Pixel positions of CI within threshold

range arranged in an array

Encryption:( Random key)

XOR

(Zero intensity position of Logo)

The positional values in the array are used to

hide the encrypted information

Decoding Technique:

Extractio n of position of hiding using

Threshold value

Extraction of the encrypted

positional information

Filtering by Majority Algorithm on the 8

retrieved sets to get the derived data set

Decryption of the rec ove r ed data

using first key

Reconstruction of the message logo

with the receiver keys

Watermarked image

Recovered f i n a l information

logo/ watermark

3. Image Quality Metrics

To measure the amount of visual quality degradation be-

tween origina l and watermarked images, diff erent types o f

image quality metrics are used. In the present work we

have used peak signal-to-noise ratio (PSNR) and struc-

tural similarity index measure (SSIM).

3.1. Peak Signal-to-Noise Ratio (PSNR)

It is the ratio between the maximum possible power of a

signal and the power of corrupting noise that affects the

fidelity of its representation. PSNR is usually expressed

in terms of dB for a wide range of signals The PSNR is

most commonly used as a measure of quality of recon-

struction for lossy compression. The cover image in this

case is the original data, and the information logo is the

error introduced by watermarking. When comparing the

deformed image with the original one an approximation

to human perception of reconstruction quality is made.

Therefore in some cases one reconstruction may appear

to be closer to the original than another, even though it

has a lower PSNR. So a higher PSNR would normally

indicate that the reconstruction is of higher quality.

It is most easily defined via the mean square error

(MSE) which for two m × n monochrome images I and K,

where one of the images is considered a noisy approxi-

mation of the other, is defined as:

 

11 2

1,,

SEI i jK i j

















The PSNR is defined as:

10log

20log

MAX

PSNR













here, MAX1 is the maximum possible pixel value of the

image. When the pixels are represented using 8 bits per

sample, this is 255.

3.2. Structural Similarity Index Measure (SSIM)

It is a method for measuring the similarity between two

images. The SSIM index is a full reference metric, where

the measure of the image quality is based on an initial

distortion-free image as reference. SSIM is designed to

improve on traditional methods like PSNR and MSE,

which have proved to be inconsistent with human eye

perception. The resultant SSIM index is a decimal value

between –1 and 1. The value 1 is only reachable in the

case of two identical sets of data. The SSIM metric is

calculated on various w indows of an image. The measure

between two windows x and y of common size N × N is:

Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique

203







22 22

,xy xy

xy xy

SSIMx ycc

 

 



 In Section 4.4, eight different sets of the recovered

logo and the final constructed logo using majority algo-

rithm are given.

where μx is the average of x; μy the average of y; 2



the

variance of x; 2



the variance of y; σxy the covariance

of x and y; c1 = (k1L)2, c2 = (k2L)2 are two variables to

stabilize the division with weak denominator; L the dy-

namic range of the pixel-values ( typically this is 2# bits per

pixel – 1); k1 = 0.01 and k2 = 0.03 by default.

4.1. Embedding of Watermark into Cover Image

and Quality Metrics

It can be observed from Table 1 that the results obtained

from the quality metrics are very satisfactory and hence

we can conclude fr om the obtained dat a t hat t h e wat ermarked

image is not very much different from the original cover

image that is being used. Also, the difference between

the watermarked image and the original appears almost

the same to the human eye.

4. Results and Discussions

In this section several experimental results are given to

show the outcome of the proposed watermarking tech-

nique. Higher PSNR value indicates good quality of picture.

After embedding the information logo in the cover image

for Lena, Tower and Fruit images we find that the PSNR

value is quite high .

In Section 4.1 three sets of cover image along with

three information logos are taken as input. The water-

marked image is shown after embedding. The computed

value of the quality metrics are also given to find the

image quality.

Similarly SSIM is another measuring metric used for

finding the similarity between the two images. Here we

observe that after embedding the information logo the

similarity between the cover image and watermarked

image is 0.98 which describes a good structural similar-

ity between these two images.

In Section 4.2, the watermarked images and the recov-

ered information logos are given.

In Section 4.3, the outcome for the same recovery

technique is shown but under two attacks known as salt

and pepper noise and image compression. For salt and

pepper noise the percentage is varied up to 40% and

compression up to 5%. The required noisy watermarked

images and the recovered logo from those images are

presented.

4.2. Recovery of Watermark from Watermarked

Image without Any Attack

Here the hidden watermarked image i.e. the information

logo is successfully recovered from the un-attacked

Table 1. Watermark embedding and image quality metrics.

Cover Image (dim-256 × 256) Message Image (dim-16 × 16)Watermarked Image (dim-256 × 256)PSNR in dB SSIM

42.343 0.9889

Lena S logo Watermarked Lena

41.806 0.9781

Tower K logo Watermarked Tower

41.506 0.9853

Fruit Max Payne logo Watermarked Fruit

Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique

204

watermarked image which is given in Table 2. Primarily,

we have considered the communication is ideal and hence

no external interference or attack has been included.

In practice, however, we have to consider noise, which

is dealt with in the next section.

4.3. Recovery of Watermark from Watermarked

Image under Attacks

In watermarking terminology, an attack is an y processing

that may impair detection of the watermark or commu-

nication of the information conveyed by the watermark.

The processed watermarked data is then called attacked

data. There are two kinds of watermark attacks: Non-

intentional attacks, such as compression of a legally ob-

tained, watermarked image or video file, and intentional

attacks, such as an attempt by a multimedia pirate to de-

stroy the embedded information and prevent tracing of

illegal copies of watermarked digital video.

The present work describes the following two types of

attack:

(1) Salt and pepper noise.

(2) JPEG Compression.

(1) Salt and pepper noise:

In this section we have demonstrated the proposed

watermarking technique after using the salt and pepper

noise to corrupt the watermarked images up to 40%.

From the above set of results in Table 3, it is clear that

that the proposed algorithm can withstand 40% salt and

Table 2. Recovered watermark or hidden message from

un-attacked watermarked image.

Watermarked Image

(dimension 256 × 256) Recovered Message Image

(dimension 16 × 16)

Lena S logo

Tower K logo

Fruit Max Payne logo

Table 3. Recovered watermark or hidden message from salt

and pepper noise attacked watermarked image.

Watermarked

Image

(dimension 256 × 256)

Attacked

Image

( Salt and Pepper Noise)

Recovered

Message Image

(dimension 16 × 16)

Lena 10% S logo

Lena 20% S logo

Lena 30% S logo

Lena 40% S logo

pepper attack with ease and the information logo that is

derived from the watermarked image closely resembles

the information logo that was embedded into the image.

Hence we can say that the proposed algorithm efficiently

handles salt and pepper noise.

Similarly in the next Table 4, the strength of the pro-

posed algorithm is demonstrated against the salt and

pepper attack with a different set of data.

(2) JPEG Compression:

The performance of the proposed algorithm is also

demonstrated against JPEG compression attack in Table

5. This algorithm also demonstrates its strength against

compression attack as well.

4.4. Construction of Different Recovered Logo

Using Majority Algorithm Technique

Tables 6 and 7 describe the outcome of majority algo-

rithm technique. Here final constructed S logo and K

logo from 8 different recovered sets have been shown. It

is clearly visible that the proposed majority algorithm

technique is strong enough to construct the information

logo from some distorted sets of recovered logo.

Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique 205

Table 4. Recovered watermark from salt and pepper noise

attacked watermarked image.

Watermarked

Image Attacked Image

(Salt and Pepper Noise) Recovered

Message Image

Fruit 10% Max Payne

Fruit 20% Max Payne

Fruit 30% Max Payne

Fruit 40% Max Payne

Table 5. Recovered watermark from JPEG compressed

watermarked image.

Watermarked

Image Attacked Image (JPEG

Compression) Recovered

Message Image

Fruit Quality = 99% Max Payne

Tower Quality = 98% K logo

Lena Quality = 95% S logo

Table 6. Derived S logo from 8 sets of recovered noisy logo

using majority algorithm.

Recovered

S logo

from 1st set

Recovered

S logo

from 2nd set

Recovered

S logo

from 3rd set

Recovered

S logo

from 4th set

Recovered S

logo from 5th

set

Recovered S

logo from 6th

set

Recovered S

logo from 7th

set

Recovered S

logo from 8th

set

Derived

S logo

from these

8 set using

Majority

Algorithm

Table 7. Derived K logo from 8 sets of recovered noisy logo

using majority algorithm.

Recovered

K logo

from 1st set

Recovered

K logo

from 2nd set

Recovered

K logo

from 3rd set

Recovered

K logo

from 4th set

Recovered

K logo

from 5th set

Recovered

K logo

from 6th set

Recovered

K logo

from 7th set

Recovered

K logo

from 8th set

Derived

K logo

from these

8 set using

Majority

Algorithm

SSIM is used for finding the similarity between the two

images. Table 8 describes the quality of the recovered

logo. The similarity between the original logo and the

recovered logo from the watermarked image is measured

using SSIM. The following results describe that the pro-

posed algorithm is quite efficient for salt and pepper

noise up to 40% and JPEG compression resulting in im-

age quality distortion = 95%.

5. Conclusions

In present paper the proposed algorithm for digital

watermarking aims at obtaining a solution to the several

problems of digital communication and also for data

hiding. It is seen that the proposed algorithm is robust

against compression and “salt and pepper” noise attacks

where a private key is required for the recovery of the

hidden information and which enhances security to the

algorithm. Since digital watermarking has many app-

lications in the digital world toda y it can be thought of as

a digital communication scheme where an au xiliary mes-

sage is embedded in digital multimedia signals and is

available wherever the latter signals move.

The results obtained show satisfactory statistics of the

performance of the proposed algorithm. The obtained

PSNR and SSIM values support the quality of the en-

cryption method. It is also seen that the embedded inf orma-

Reversible Di gital Image Wate rmarking Scheme Using Bit Replacement and Majority Algorithm Technique

206

Table 8. SSIM values for different sets of recovered water-

mark or information logo under salt and pepper noise and

JPEG compression attack.

Used Logo

(dimension 16×16) Types of AttackAmount of

Distortion SSIM

20% 0.9554

30% 0.9032

S Logo Salt and

Pepper Noise

40% 0.7954

Quality = 99% 0.9687

Quality = 98% 0.8654 S Logo JPEG

Compression

Quality = 95% 0.7496

mation is successfully recovered from the watermarked

image by using the majority algorithm technique. We can

conclude by stating that the proposed algorithm provides

a method for secure data hiding.

6. Acknowledgements

It is my pleasure to express my gratitude to all of the

faculty members of Institute of Radio physics and Elec-

tronics, University of Calcutta, Kolkata.

I am very much thankful to all the of the facu lty mem-

bers of Electronics and communication Department, prin-

cipal and the authority of Guru Nanak Institute of Tech-

nology, S o d e po r e , K o lk a t a f o r t h e i r un grudgi ng support.

I am also very grateful to my fami ly memb er s fo r t he ir

continuous encouragement.

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